Biological signal is a general term that refers to the signal generated by a biological system. Depending on the measurement devices and recording methods in question, biological signals can take a variety of forms. One common type of biological signal is the bioelectric signal generated by the biological organs, tissues, or cells, for example, the body surface electrocardiogram (ECG) signal, the intracardiac electrogram (IEGM) signal, the scalp electroencephalogram (EEG) signal, the intracranial electrocorticogram (ECOG) signal, the electromyogram (EMG) signal, the gastric and intestinal electrogram, the somatosensory evoked potential (visual, auditory, motor) signal, neuronal or cellular action potential signal, etc. Another common type of biological signal is the impedance signal that is derived from voltage measurement after injecting a current to the biological tissue, for example, the intracardiac impedance signal, the thransthoracic (across lung) impedance signal, etc. Yet another common type of biological signal is the pressure signal within a biological compartment, for example, the intracardiac (atrial and ventricular) blood pressure, the central and peripheral blood pressure, the intra-bladder pressure, the intra-uterine pressure, the intra-gastric pressure, the intra-cranial pressure, partial gas (e.g., oxygen) pressure, etc. Other types of biological signals include acoustic signals (e.g., heart sound, intracardiac or thransthoracic ultrasonic measurement, output of acoustic sensor in cochlea implant, etc.), temperature signals (e.g., core and peripheral body temperature, organ temperature), volume signals (e.g., cardiac output, cardiac stroke volume, respiratory tidal volume, etc.), chemical signals (e.g., glucose concentration, drug concentration, the pH level, etc.), event frequency signals (e.g., heart rate, respiration frequency, firing rate of action potentials, etc.), movement signals (e.g., accelerometer output, posture measurement, gait measurement), body weight signals, and so on. In addition, these directly measured biological signals can be further processed to generate secondary biological signals, for example, a composite signal derived from multiple measured biological signals, a frequency spectrum calculated from the measured time-domain signals, and so on.
Many biological signals show periodic variations. For example, the cardiac signal and blood pressure signal vary with heart beat cycles, the pulmonary signals vary with respiratory cycles, and many other biological signals show circadian variations. Even without apparent periodic variations, some biological signals show distinctive waveform morphology. Some representative examples include the myocardial evoked potentials after cardiac pacing, the somatosensory evoked potentials after external stimuli, the action potentials of neural or muscle cells, etc. These distinctive signal patterns reflect the dynamics of the biological systems. Therefore, useful information regarding the status of the biological systems can be probed by analyzing these signals patterns.
Numerous medical devices and systems have been developed to measure various types of biological signals by using different types of sensors. A common feature of these medical devices and systems is to provide diagnostic information regarding the biological system by analyzing the biological signals. Another common feature is to deliver appropriate therapies to the biological system according to the results of biological signal analysis. Therefore, accurate and efficient biological signal analysis is crucial for the normal operation of the medical devices and systems. Due to the distinctive patterns of many biological signals, one typical biological signal analysis technique is correlation analysis, which quantitatively measures the association between two signals. It has numerous applications, including morphological analysis, signal classification, pattern recognition, and so on.
One application of signal classification is capture detection or verification by recognizing a characteristic electrogram waveform after delivery of an electrical stimulation pulse (pace).
Implantable pulse generators (IPGs) such as pacemakers or defibrillators/cardioverters (ICD) can help ensure proper contractions of one or more heart chambers if proper natural contraction of the heart chambers is affected by a disease. A contraction of a heart chamber can be induced by an electrical stimulation pulse generated by an IPG. Depending on the chambers of a heart to be stimulated, single, dual or more chamber pacemakers, e.g. three chamber biventricular pacemakers, are known.
In a healthy heart, initiation of the cardiac cycle normally begins with depolarization of the sinoatrial (SA) node. This specialized structure is located in the upper portion of the right atrium wall and acts as a natural “pacemaker” of the heart. In a normal cardiac cycle and in response to the initiating SA depolarization, the atrium contracts and forces the blood that has accumulated therein into the ventricle. The natural stimulus causing the atrium to contract is conducted to the ventricle via the atrioventricular (AV) node with a short, natural delay, the atrioventricular delay (AV-delay). Thus, a short time after an atrial contraction (a time sufficient to allow the bulk of the blood in the atrium to flow through the one-way valve into the ventricle), the ventricle contracts, forcing the blood out of the ventricle to body tissue. A typical time interval between contraction of the atrium and contraction of the ventricle might be 180 ms; a typical time interval between contraction of the ventricle and the next contraction of the atrium might be 800 ms. Thus, in a healthy heart providing proper AV-synchrony, an atrial contraction (A) is followed a relatively short time thereafter by a ventricle contraction (V), that in turn is followed a relatively long time thereafter by the next atrial contraction and so on.
To mimic the natural behavior of a heart, a dual-chamber pacemaker, in conventional manner, defines a basic atrial escape interval (AEI) that sets the time interval for scheduling an atrial stimulation pulse. The atrial escape interval can be started by a ventricular event and end with an atrial event. A basic AV delay (AVD) or ventricular escape interval (VEI) sets the time interval or delay between an atrial event and a ventricular event. In such embodiment, AEI and AVD (or VEI) thus together define a length of a heart cycle which is reciprocal to the pacing rate at which stimulation pulses are generated and delivered to a patient's heart in the absence of sensed natural cardiac activity.
Depending on the mode of operation, a pacemaker only delivers a stimulation pulse (pacing pulse) to a heart chamber (atrium or ventricle) if needed, that is, if no natural excitation of that chamber occurs. Such mode of operation is called an inhibited or demand mode of operation since the delivery of a stimulation pulse is inhibited if a natural excitation of the heart chamber is sensed within a predetermined time interval (usually called escape interval) so the heart chamber is only stimulated if demanded.
In a demand mode, the pacemakers monitors the heart chamber to be stimulated in order to determine if a cardiac excitation (heartbeat) has naturally occurred. Such natural (non-stimulated) excitation, also referred to as “intrinsic” cardiac activity, is manifested by the occurrence of recognizable electrical signals that accompany the depolarization or excitation of a cardiac muscle tissue (myocardium). The depolarization of the myocardium is usually immediately followed by a cardiac contraction. For the purpose of the present application, depolarization and contraction may be considered as coupled events and the terms “depolarization” and “contraction” are used herein as synonyms. The recognizable electrical signals that accompany the depolarization or excitation of a heart chamber are picked up (sensed) by the atrial or the ventricular sensing channel, respectively. Thus, one or more intracardiac electrograms (IEGMs) are acquired.
After delivery of a successful stimulation pulse a characteristic electrical signal can be recorded, called the evoked response. After delivery of an unsuccessful stimulation pulse not leading to capture, no evoked response can be recorded (apart from a different characteristic electrical signal called the polarization artifact.)
A natural contraction of a heart chamber can also be detected by evaluating electrical signals sensed by the sensing channels. In the sensed electrical signal the depolarization of an atrium muscle tissue is manifested by occurrence of a signal known as “P-wave”. Similarly, the depolarization of ventricular muscle tissue is manifested by the occurrence of a signal known as “R-wave”. A P-wave or a R-wave represents an atrial event or a ventricular event, respectively, in the further course of this application.
For the purpose of this application, a “ventricular event” (V) may refer either a natural ventricular excitation (intrinsic ventricular event; Vs) which is sensed as an R-wave or a ventricular stimulation pulse (V-pulse, Vp). Similarly, an atrial event (A) shall refer to both a P-wave (A-sense; As) or an atrial stimulation pulse (A-pulse, Ap).
An implantable cardiac device, particularly an artificial cardiac pacemaker, “captures” the heart by delivering an appropriately timed electrical pulse designed to cause contraction of the myocardium of the heart. To ensure capture, the strength and duration of the stimulation pulse should be adjusted so that the energy being delivered to the myocardium exceeds a threshold value. On the other hand, it is desirable for the pulse energy not to be higher than is needed for a reasonable safety margin for longer battery life. Because the threshold for capture varies from patient to patient, and can change over time for the same patient, it is also desirable that the pulse energy delivered by the pacemaker be adjustable during and subsequent to implantation. Although periodic adjustment can be made manually through use of an external programmer that communicates with the implanted pacemaker, it would be preferable to provide a pacemaker that adjusts the pulse energy automatically and dynamically in response to changes in the capture threshold. This functionality requires the pacemaker to be able to verify capture after the delivery of pacing pulse, and has the capability of automatically testing the capture threshold.
Many different approaches have been taken during the past decade to develop the pacemakers with “auto-capture control” functionality. Theoretically, capture verification could be accomplished by detecting the existence of evoked potential that manifests in a characteristic ECG waveform, which should follow a capturing pulse but not the non-capturing pulse. In practice, however, reliable detection of the evoked potential is quite challenging because the after-potentials and electrode-tissue polarization artifacts resulting from the pacing pulse mask the evoked response and change the ECG waveform, and also saturate the sense amplifiers coupled to the electrodes, until they dissipate. By the time that the sense amplifier is no longer blinded, the evoked response, if any, has typically passed the electrodes.
Because of these technical difficulties, so far there has been only limited success in beat-by-beat capture classification for RV pacing based on evoked potential analysis. For the RA pacing, and for the RV and LV pacing in the CRT and CRT/D devices, currently there is no solution that can perform capture classification on a beat-by-beat basis.
Atrial capture detection based on evoked potential analysis is technically more challenging than RV capture detection because the amplitude of the atrial evoked potential is much smaller and more difficult to discriminate from noise, other cardiac signals, and the residual polarization charge on the electrode.
For CRT (cardiac resynchronization therapy) and CRT-D/CRT+ defibrillator devices, the RV and LV capture detection based on evoked potential analysis is also difficult, particularly when the device is programmed with a V-V delay not equal to zero. Each ventricular pace not only triggers a blanking window (during which ECG signals are either not recorded or ignored) in the same ventricular channel, but also starts a blanking window (cross-channel blanking) in the other ventricular channel. As a result, the non-blanked ventricular electrogram that is available for analysis has shortened duration, and the evoked potential waveform, which is crucial for capture detection, is often blanked.
Furthermore, it has been known that the RV capture detection based on evoked potential analysis (waveform of the ECG after stimulation pulse delivery) can be negatively affected by the ventricular fusion (simultaneous occurrence of RV pace and the antegrade conducted wave). For the CRT and CRT/D devices, the fusion beat poses a more challenging problem for the capture detection, because one ventricle chamber not only can be activated by pacing, it may also be activated by the wave conducted from the other ventricle chamber.
Instead of solely relying on evoked potential analysis for capture classification, alternative methods have been developed to analyze the pace waveform, possibly including part of the T wave, for capture classification (U.S. Pat. Nos. 6,324,427, 7,006,869, 7,086,603, 7,139,610, 7,162,301, 7,177,689). Generally, the template waveform corresponding to captured beats is created. Capture is declared if a paced signal waveform matches the template, otherwise, non-capture is declared. Whether or not a waveform matches the template can be evaluated based on simple morphological metrics such as wave width, height, zero-crossing, area, etc., or based on conventional correlation analysis techniques, such as the Pearson's correlation coefficient (see below). However, all these methods are not suitable for implementation in the implant device due to many limitations. For example, the template waveform may be contaminated by the pacing artifacts caused by paces from one or multiple chambers; the template waveform may not be comparable to the test waveform due to different device parameter settings, such as various V-V delays and pace blanking periods; the methods used for assessing similarity between template waveform and test waveform either have low sensitivity and specificity (e.g., morphological metrics), or require high computational cost (e.g., Pearson's correlation coefficient); and furthermore, there is lack of strategy regarding how the implant device shall automatically and rationally manage its operation based on continuous capture classification.
Thus there is a need for the implant device to implement an integrated yet efficient strategy, to continuously monitor the pacing capture status in all three chambers (RA, RV, LV), to confirm the pacing capture, to detect non-capture paces, and accordingly adjust the pacing parameters to ensure capture.
One of the most commonly used indices for correlation analysis is the Pearson's correlation coefficient (CC), which provides a quantitative measure of the linear relationship between two signals (one-dimensional, two-dimensional, or multi-dimensional). Considering X=[x1, x2, . . . , xL] and Y=[y1, y2, . . . , yL] are two one-dimensional vectors of length L, and each has zero mean, their CC is defined as:
  CC  =                    X        ·        Y                                        X                          ·                            Y                                =                            ∑                      i            =            1                    L                ⁢                              x            i                    ·                      y            i                                                            ∑                          i              =              1                        L                    ⁢                                    x              i              2                        ·                                                            ∑                                      i                    =                    1                                    L                                ⁢                                  y                  i                  2                                                                        
The CC approaches 1 when there is a positive linear relationship between X and Y, approaches −1 when there is a negative linear relationship between X and Y, and is some value in between otherwise, indicating the degree of linear dependence between the two vectors. Consequently, the CC has been frequently used to quantify the similarity between two signals. That is, two signals X and Y are considered increasingly similar when their CC is approaching 1. Conversely, X and Y are considered not similar for decreasing CC, and in opposite phase when their CC is −1.
However, there are several limitations for using CC as a measure of signal similarity. First, the calculation of CC usually requires extensive floating-point operation, which renders it impractical for implementation in low-power devices or systems such as battery-powered implantable medical devices. Second, the CC is less sensitive to amplitude discrepancy between the signals. For example, the CC between two signals X and Y=ρ·X, where ρ is a constant scaling factor, is always 1, despite the fact that the amplitude of Y can be significantly different than that of X. Thirdly, the CC between two signals is affected by each sample amplitude of each signal, and thus is sensitive to additive noise such as impulse noise or continuous random noise.
Therefore, there is a need for a new methods and devices for quantitatively, efficiently, and robustly measuring the similarity between biological signals.